21 papers · ranked by Valyu relevance
Nathan Shaw, Sanjeetha Pennada, Robert M Hierons, Donghwan Shin
As Autonomous Driving Systems (ADS) progress towards commercial deployment, there is an increasing focus on ensuring their safety and reliability. While considerable research has been conducted on testing methods for detecting faults in ADS, very little attention has been paid to debugging in ADS. Debugging is an…
Daisuke Akiba
This theoretical paper introduces the Verbal-Cognitive Scaffold (VCS) Model, a cognitively inclusive framework which proposes the cognitive architectures underlying computational thinking (CT). Moving beyond monolithic theories of cognition (e.g., executive-function and metacognitive control models), the VCS Model…
Jiatong Liu, Xue Yao, Zehua Zhang, Yongqiang Tian
Debugging exercises are often assessed from final code and test outcomes, yet these artifacts hide how students reproduced failures, formed hypotheses, inspected evidence, edited code, and verified fixes. We present DebugTracker, a Visual Studio Code extension that records lightweight debugging-process evidence for…
Yunkun Wang, Yue Zhang, Guochang Li, Zhi + 5 more
Large Language Models (LLMs) frequently generate buggy code with complex logic errors that are challenging to diagnose. While existing LLM-based self-repair approaches conduct intensive static semantic analysis or reply on superficial execution logs, they miss the in-depth runtime behaviors that often expose bug root…
Kehao Mao, Baokun Hu, Ruixin Lin, Zewen Li + 2 more
Automated programming has become a powerful tool for solving real-world problems. Code generation, in particular, plays a key role in improving developer productivity and reducing the entry barrier to software development. Recent advances in large language models (LLMs) have significantly improved program synthesis…
Maolin Sun, Yibiao Yang, Xuanlin Liu, Yuming Zhou + 1 more
Patching severe security flaws in complex software remains a major challenge. While automated tools like fuzzers efficiently discover bugs, fixing deep-rooted low-level faults (e.g., use-after-free and memory corruption) still requires labor-intensive manual analysis by experts. Emerging Large Language Model (LLM)…
C. Gómez, Fábio Petrillo
Conventional debugging techniques used in traditional software are similarly used when debugging video games. However, the reality of video games require its own set of unique debugging techniques such as On-Screen Console, Debug Draws, Debug Camera, Cheats and In-Game Menus, and Data Scrubbing. In this article, we…
Charaka Geethal Kapugama
—This paper introduces DDMIN-LOC, a technique that combines Delta Debugging Minimization (DDMIN) with Spectrum-Based Fault Localization (SBFL). It can be applied to programs taking string inputs, even when only a single failure-inducing input is available. DDMIN is an algorithm that systematically explores the minimal…
Maarten Steevens, Tom Lauwaerts, Christophe Scholliers
Debugging nondeterministic programs is inherently difficult, particularly in microcontroller environments where execution paths can diverge unpredictably due to external sensor inputs. Traditional debugging techniques often fail to capture or reproduce this nondeterministic behavior effectively. Multiverse debugging…
Liu, Boyang
—In order to meet the needs of students' programming debugging ability training, this paper designs and implements a data acquisition and analysis system for programming debugging process based on VS Code plug-in,which aims to solve the limitation of traditional assessment methods that are difficult to fully evaluate…
Ying Dai
In recent years, generative artificial intelligence (GenAI) tools such as ChatGPT have been increasingly integrated into academic reading in higher education. Although GenAI can support processing complex academic texts, its effective use requires learners to employ metacognitive strategies to avoid uncritical…
JungWoo Park, Minju Kang, Seungho Jeon, Seong Oun Hwang + 4 more
Fault localization (FL) is the task of identifying code locations responsible for bugs in software, and it is a prerequisite step in the bug-fixing process. FL in large-scale systems such as the Linux kernel involves three core challenges: First, the vast codebase fundamentally complicates fault search. Second, the…
Takahito Mukai, Aika Ohishi, Emi Hagiuda, Keita Shimamoto + 2 more
Genome synthesis is a major limitation in generative biology. Here, the half-sized genome of Escherichia coli was constructed by fleshing out an imperfect minimal genome through genome-scale debugging process. Our platform consists of integrated development environment (IDE) and runtime environment (RTE). The genome…
Jaeyong Lee, Zuwan Lin, Wenbo Wang, Jongmin Baek + 7 more
The development of flexible bioelectronics remains a complex, multidisciplinary process that demands specialized expertise and labor-intensive efforts, limiting scalability, adaptability and accessibility. Here, we introduce DeviceAgent, an autonomous multimodal AI agent that integrates large language models (LLMs)…
Muhammad Zain Butt, Rana Sheraz Ahmad, Eman Fatima, Muhammad Tahir ul Qamar
The application of Large Language Models (LLMs) for generating data visualizations through natural language interaction represents a promising advance in AI-assisted scientific analysis. However, existing LLM-based tools largely emphasize graph generation, while research workflows require not only visualization but…
Juyong Shin, Jisu Kim, Jaehyun Nam, Claudio Savaglio
System call telemetry is essential for understanding runtime behavior in cloud-native infrastructures, but existing eBPF-based monitors suffer from high per-event overhead, unreliable delivery under load, and limited context for correlating multi-step activities. These issues reduce scalability, create blind spots in…
Authors not listed
Chemistry curricula often separate “wet” experimental work from “dry” computation, yet modern discovery increasingly demands both. This Perspective offers an instructor-ready roadmap to train “hybrid chemists” within existing courses. We distill recent advances in machine learning, automation, and real-time analytics…
Ryan Aalund, Vincent P. Paglioni, Ying Zheng, Ke Zhang + 2 more
IoT devices operate as integrated systems spanning hardware, firmware/software layers, and communication layers. In operational settings, many faults and performance degradations are emergent: they arise from cross-layer interactions, workload changes, and telemetry artifacts, rather than a single physics-of-failure…
Shreyansh Agrawal, Harsh B. Anadkat, Kiran K. Athimoolam, Harsh Bhardwaj + 8 more
Recent advances in artificial intelligence (AI) have prompted claims about autonomous “AI scientists,” yet systematic evaluations of these capabilities remain scarce. This exploratory study investigates whether current AI frameworks can execute scientific research tasks beyond isolated demonstrations. We tested eight…
Hung Q. Vo, Huy Q. Vo, Son T. Ly, Zhihao Wan + 5 more
Conventional tissue image analysis software provides foundational capabilities for cellular analysis, including segmentation, basic morphological feature extraction, and spatial organization analysis. However, these tools often require manual intervention and are not well integrated with code-driven automation…
Authors not listed
We present burbuja (Baring Unseen Regions of Bubbles Using Joint-Density Analysis), an automated software tool for detecting and characterizing gas bubbles and other local voids in molecular structures and trajectories containing explicit aqueous solvent. We describe the burbuja algorithm and demonstrate its accuracy…